R workflow for spot analysis

This workflow is designed to take raw Spot output (.csv file) and automatically perform:

√ Import csv file
√ Extraneous row deletion
√ Tripicate averaging
- Background subtraction
- Statistics (one-way ANOVA with post-hoc test)
- Data visualisation

Install Packages

install.packages("dplyr")
install.packages("tidyverse")

Load Libraries

library(dplyr)
library(tidyverse)


Set the Working Directory

setwd("/Volumes/DOOLAN_Research_Team-A13379/IMB team/LAB MEMBERS/JONATHAN TAN/Projects/ANOVA_tutorial/ANOVA_tutorial")

Add any extra directories

dir.create("plots")
dir.create("data")


Import csv

read.csv("Data/P6_spots.csv", header = FALSE) # header = FALSE is included to avoid the error: more columns than column names
##                                     V1  V2  V3  V4  V5  V6  V7  V8  V9 V10 V11
## 1                         GU001-P6.plt  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA
## 2                            enzymatic  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA
## 3  Friday, September 06, 2024 11:51:51  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA
## 4                                        1   2   3   4   5   6   7   8   9  10
## 5                                    A   2   3   1   2   2   1   1   0   0  26
## 6                                    B 188 165 160 119 164 126  90  87 102 109
## 7                                    C  42  61  61  97  88  92  60  61  44  51
## 8                                    D 530 517 487 650 689 672 349 354 318 321
## 9                                    E 440 527 562 665 640 646 340 311 307 353
## 10                                   F  31  22  28  32  13  35  35  16  16  18
## 11                                   G 508 497 516 596 638 576 352 335 341 389
## 12                                   H 557 550 639 610 637 668 674 649 678 663
##    V12 V13
## 1   NA  NA
## 2   NA  NA
## 3   NA  NA
## 4   11  12
## 5    0   1
## 6  117 131
## 7   49  33
## 8  354 296
## 9  339 328
## 10   9   7
## 11 387 378
## 12 586 643
P6<-read.csv("Data/P6_spots.csv", header = FALSE) # Create a new variable (P6) and save the contents of the .csv file into that variable


Remove extraneous rows (subsetting)

P6_clean <- slice(P6, -c(1:4)) # Remove (slice) rows 1-4
colnames(P6_clean) <- c("Plate_rows", "Mouse1_r1","Mouse1_r2","Mouse1_r3","Mouse2_r1","Mouse2_r2","Mouse2_r3","Mouse3_r1","Mouse3_r2","Mouse3_r3","Mouse4_r1","Mouse4_r2","Mouse4_r3") # Change to meaningful header names (r1 = replicate 1)
print (P6_clean)
##   Plate_rows Mouse1_r1 Mouse1_r2 Mouse1_r3 Mouse2_r1 Mouse2_r2 Mouse2_r3
## 1          A         2         3         1         2         2         1
## 2          B       188       165       160       119       164       126
## 3          C        42        61        61        97        88        92
## 4          D       530       517       487       650       689       672
## 5          E       440       527       562       665       640       646
## 6          F        31        22        28        32        13        35
## 7          G       508       497       516       596       638       576
## 8          H       557       550       639       610       637       668
##   Mouse3_r1 Mouse3_r2 Mouse3_r3 Mouse4_r1 Mouse4_r2 Mouse4_r3
## 1         1         0         0        26         0         1
## 2        90        87       102       109       117       131
## 3        60        61        44        51        49        33
## 4       349       354       318       321       354       296
## 5       340       311       307       353       339       328
## 6        35        16        16        18         9         7
## 7       352       335       341       389       387       378
## 8       674       649       678       663       586       643
# write.csv(P6_clean, "Data/P6_clean.csv")


Average triplicate wells

P6_average <- pivot_longer(P6_clean, cols = starts_with("M"), names_to = "replicates", values_to = "counts") |> # Convert dataframe into "Tall format" by concatenating columns (MouseX_rX) into one column (replicates)
  mutate(group = gsub(pattern = "\\_r[1|2|3]", replacement = "", x = replicates)) |> # Search/match headers for the characters/pattern xxx_r1 etc, substitute/replace with nothing "" (ie. Mouse1). Search within "replicates"
  group_by(Plate_rows, group) |> mutate(avg = mean(counts)) |> 
  distinct(Plate_rows,group,avg) |> 
  pivot_wider(id_cols = Plate_rows, names_from = group, values_from = avg) |> as.data.frame()
print (P6_average)
##   Plate_rows    Mouse1     Mouse2      Mouse3    Mouse4
## 1          A   2.00000   1.666667   0.3333333   9.00000
## 2          B 171.00000 136.333333  93.0000000 119.00000
## 3          C  54.66667  92.333333  55.0000000  44.33333
## 4          D 511.33333 670.333333 340.3333333 323.66667
## 5          E 509.66667 650.333333 319.3333333 340.00000
## 6          F  27.00000  26.666667  22.3333333  11.33333
## 7          G 507.00000 603.333333 342.6666667 384.66667
## 8          H 582.00000 638.333333 667.0000000 630.66667


Subtract background

The purpose of this step is to subtract “Media only” spot counts from Peptide and ConA stimulation wells, and to subtract A20-empty_vector counts from A20-CSP wells. The stimulants correspond to the following Plate_rows:

A(1) = Media only
B(2) = Peptide (57-70)
C(3) = Peptide (58-67)
D(4) = Peptide (280-288)
E(5) = Peptide (280-295)
F(6) = A20 (EV)
G(7) = A20 (CSP)
H(8) = ConA

We will start by selecting the rows we are subtracting from (eg. Peptides and ConA corresponding to rows [2:5, 8]), creating another dataframe with “Media only” data (row 1, replicated 5 times), then subtracting one df from the other.

P6_peptide <- P6_average[c(2:5, 8), -1]- P6_average[rep(1,5),-1] # remove first column (-1)
print (P6_peptide)
##      Mouse1    Mouse2    Mouse3    Mouse4
## 2 169.00000 134.66667  92.66667 110.00000
## 3  52.66667  90.66667  54.66667  35.33333
## 4 509.33333 668.66667 340.00000 314.66667
## 5 507.66667 648.66667 319.00000 331.00000
## 8 580.00000 636.66667 666.66667 621.66667

Repeat this for A20 subtraction:

P6_A20 <- P6_average[7, -1]- P6_average[6, -1] # remove first column (-1)
print (P6_A20)
##   Mouse1   Mouse2   Mouse3   Mouse4
## 7    480 576.6667 320.3333 373.3333

And merge the dataframes together!

P6_subtracted <- rbind(P6_peptide, P6_A20)
print (P6_subtracted)
##      Mouse1    Mouse2    Mouse3    Mouse4
## 2 169.00000 134.66667  92.66667 110.00000
## 3  52.66667  90.66667  54.66667  35.33333
## 4 509.33333 668.66667 340.00000 314.66667
## 5 507.66667 648.66667 319.00000 331.00000
## 8 580.00000 636.66667 666.66667 621.66667
## 7 480.00000 576.66667 320.33333 373.33333